Senior Platform Engineer

New

JobgetherMachine learning infrastructure

Fully remote work opportunity in Brazil.Full-TimeSenior

Salary not disclosed

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Job Details

Experience: 7+ years of relevant professional experience in software, data, platform, or infrastructure engineering.
Required Skills: AWSDockerPythonApache AirflowKafkaKubernetesGoCI/CDLinuxDistributed Systems

Bachelor’s degree in Computer Science or a related technical field.
7+ years of relevant professional experience in software, data, platform, or infrastructure engineering.
Strong knowledge of several relevant technologies, such as AWS, Python, Golang, Kafka, Airflow, Docker, Linux, and Kubernetes.
Experience working with very large data volumes and building systems that operate at significant scale.
Strong understanding of distributed systems architecture and the trade-offs involved in designing scalable services.
Knowledge of CI/CD principles and software delivery best practices.
Practical experience with Docker and/or Kubernetes-based development and orchestration.
Experience using AI technologies to improve decision-making, workflows, processes, and efficiency.
Experience creating automated, scalable infrastructure or pipelines for engineering, data, or scientific teams is highly valuable.
Familiarity with relational databases, key-value stores, or Kubernetes-based environments is beneficial.
Experience with Big Data technologies such as Apache Spark is a plus.
Familiarity with AWS services such as Batch, EMR, Glue, or SageMaker is advantageous.

Design and build scalable services and infrastructure for machine learning and data-intensive products.
Develop automation tools and pipelines that help Data Scientists and engineering teams deploy, train, and evaluate models.
Build, maintain, and improve production data systems capable of handling very large event volumes.
Develop automated, scalable infrastructure and data pipelines with reliability, performance, and maintainability in mind.
Apply distributed systems principles and make architectural trade-offs when designing large-scale solutions.
Implement and maintain CI/CD practices that support reliable software delivery.
Work with cloud infrastructure, containerized environments, and orchestration technologies for production workloads.
Collaborate with Product Managers, Data Scientists, and engineers to deliver machine learning data services.
Participate in code reviews, pull requests, and engineering discussions.
Use AI technologies to improve decision-making, workflows, and operational efficiency.

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